The Impact of Beta Blockers on Survival in Cancer Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background/Objectives: Beta adrenergic signaling has been implicated in cancer progression, leading to interest in repurposing beta blockers (BBs) as adjunctive anti-cancer agents. However, clinical findings are inconsistent. This systematic review and meta-analysis evaluates the association between BB use and survival outcomes in cancer patients. Methods: A systematic search of OVID Medline, EMBASE, and CENTRAL was conducted through 13 September 2023, for studies comparing survival outcomes in solid tumor patients using BBs versus non-users. Eligible studies reported hazard ratios (HRs) for overall survival (OS), progression-free survival (PFS), or cancer-specific survival (CSS). Perioperative studies and those without BB-specific HRs were excluded. Data extraction and quality assessment were performed in duplicate using ROBINS-I. A random-effects model was used, with heterogeneity assessed by the I2 statistic. Results: Seventy-nine studies (492,381 patients) met the inclusion criteria; 2.5% were prospective. The most frequently studied cancers were breast (n = 33), ovarian (n = 30), and colorectal (n = 28). BB use was associated with improved PFS (HR 0.78, 95% CI: 0.66–0.92, I2 = 79.8%), with significance maintained after excluding high-bias studies (HR 0.74, 95% CI: 0.61–0.91, I2 = 36.6%). No significant associations were observed for OS (HR 0.99, 95% CI: 0.94–1.04, I2 = 84.9%) or CSS (HR 0.95, 95% CI: 0.91–1.00, I2 = 77.4%). Conclusions: BB use may be associated with longer PFS in cancer patients, but findings are limited by study design and heterogeneity; high-quality prospective studies are needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.040 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".